# install the package
# install.packages("remote", dependencies = T):初回だけ必要
remotes::install_github("covid19datahub/R")
## Skipping install of 'COVID19' from a github remote, the SHA1 (8943fa78) has not changed since last install.
## Use `force = TRUE` to force installation
# データを読み込むために毎回必要
# load the package
library("COVID19")
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(DT)
COVID-19データハブの目的は、COVID-19の理解を深めるのに役立つ外生変数と結合された、世界的にきめ細かな症例データを収集することによって、研究コミュニティに統一されたデータハブを提供することである。
少しインストールが難しいかもしれません.
# 世界中の国ごとのデータ
x <- covid19()
##
## Data Science for Social Impact research group, University of Pretoria
## (2020), https://github.com
##
## Public Health Agency, Sweden (2020), https://oppnadata.se
##
## Ministery of Health, Slovenia (2020), https://www.gov.si
##
## Open Government Data, Latvia (2020), https://data.gov.lv
##
## Open Government Data, Liechtenstein (2020), https://github.com
##
## Ministero della Salute, Italia (2020), https://github.com
##
## COVID19-India API (2020), https://www.covid19india.org
##
## OpenCOVID19 France (2020), https://github.com
##
## Wikipedia (2020), https://www.wikipedia.org
##
## Ministery of Health of Czech Republic (2020),
## https://onemocneni-aktualne.mzcr.cz
##
## Ministerio de Salud y Protección Social de Colombia (2020),
## https://www.datos.gov.co
##
## Swiss Federal Statistical Office (2018), https://www.bfs.admin.ch
##
## Public Health Infobase, Government of Canada (2020),
## https://health-infobase.canada.ca
##
## Epistat, Belgian Infectious Diseases (2020),
## https://epistat.sciensano.be
##
## Open Government Data, Austria (2020),
## https://info.gesundheitsministerium.at
##
## CIA - Central Intelligence Agency (2020), https://www.cia.gov
##
## Our World in Data (2020), https://github.com
##
## World Bank Open Data (2018), https://data.worldbank.org
##
## Hale Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz
## Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik
## School of Government.
##
## Johns Hopkins Center for Systems Science and Engineering (2020),
## https://github.com
##
## Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Working paper,
## doi: 10.13140/RG.2.2.11649.81763.
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.
##
## To hide the data sources use 'verbose = FALSE'.
# 世界中の都道府県レベルのデータ
# x <- covid19(level = 2)
# イタリアとアメリカの市区町村レベルのデータ
# x <- covid19(c("Italy","US"), level = 3)
wb <- c("gdp" = "NY.GDP.MKTP.CD", "hosp_beds" = "SH.MED.BEDS.ZS")
x <- covid19(wb = wb)
##
## Data Science for Social Impact research group, University of Pretoria
## (2020), https://github.com
##
## Public Health Agency, Sweden (2020), https://oppnadata.se
##
## Ministery of Health, Slovenia (2020), https://www.gov.si
##
## Open Government Data, Latvia (2020), https://data.gov.lv
##
## Open Government Data, Liechtenstein (2020), https://github.com
##
## Ministero della Salute, Italia (2020), https://github.com
##
## COVID19-India API (2020), https://www.covid19india.org
##
## OpenCOVID19 France (2020), https://github.com
##
## Wikipedia (2020), https://www.wikipedia.org
##
## Ministery of Health of Czech Republic (2020),
## https://onemocneni-aktualne.mzcr.cz
##
## Ministerio de Salud y Protección Social de Colombia (2020),
## https://www.datos.gov.co
##
## Swiss Federal Statistical Office (2018), https://www.bfs.admin.ch
##
## Public Health Infobase, Government of Canada (2020),
## https://health-infobase.canada.ca
##
## Epistat, Belgian Infectious Diseases (2020),
## https://epistat.sciensano.be
##
## Open Government Data, Austria (2020),
## https://info.gesundheitsministerium.at
##
## CIA - Central Intelligence Agency (2020), https://www.cia.gov
##
## Our World in Data (2020), https://github.com
##
## World Bank Open Data (2018), https://data.worldbank.org
##
## Hale Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz
## Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik
## School of Government.
##
## Johns Hopkins Center for Systems Science and Engineering (2020),
## https://github.com
##
## Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Working paper,
## doi: 10.13140/RG.2.2.11649.81763.
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.
##
## To hide the data sources use 'verbose = FALSE'.
# x2という名前のオブジェクトに5/17現在のデータのみを取り出してしまう.
x2 <- x %>%
filter(date=="2020-05-17")
datatable(x2, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
gmr <- "https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv"
gmr_x <- covid19(gmr = gmr)
##
## Data Science for Social Impact research group, University of Pretoria
## (2020), https://github.com
##
## Public Health Agency, Sweden (2020), https://oppnadata.se
##
## Ministery of Health, Slovenia (2020), https://www.gov.si
##
## Open Government Data, Latvia (2020), https://data.gov.lv
##
## Open Government Data, Liechtenstein (2020), https://github.com
##
## Ministero della Salute, Italia (2020), https://github.com
##
## COVID19-India API (2020), https://www.covid19india.org
##
## OpenCOVID19 France (2020), https://github.com
##
## Wikipedia (2020), https://www.wikipedia.org
##
## Ministery of Health of Czech Republic (2020),
## https://onemocneni-aktualne.mzcr.cz
##
## Ministerio de Salud y Protección Social de Colombia (2020),
## https://www.datos.gov.co
##
## Swiss Federal Statistical Office (2018), https://www.bfs.admin.ch
##
## Public Health Infobase, Government of Canada (2020),
## https://health-infobase.canada.ca
##
## Epistat, Belgian Infectious Diseases (2020),
## https://epistat.sciensano.be
##
## Open Government Data, Austria (2020),
## https://info.gesundheitsministerium.at
##
## CIA - Central Intelligence Agency (2020), https://www.cia.gov
##
## Our World in Data (2020), https://github.com
##
## World Bank Open Data (2018), https://data.worldbank.org
##
## Hale Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz
## Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik
## School of Government.
##
## Johns Hopkins Center for Systems Science and Engineering (2020),
## https://github.com
##
## Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Working paper,
## doi: 10.13140/RG.2.2.11649.81763.
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.
##
## To hide the data sources use 'verbose = FALSE'.
# gmr_x2という名前のオブジェクトに5/7のデータのみを取り出してしまう.
gmr_x2 <- x %>%
filter(date=="2020-05-07")
datatable(gmr_x2, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
amr <- "https://covid19-static.cdn-apple.com/covid19-mobility-data/"
amr <- paste0(amr, "2008HotfixDev28/v2/en-us/applemobilitytrends-2020-05-15.csv")
amr_x <- covid19(amr = amr)
##
## Data Science for Social Impact research group, University of Pretoria
## (2020), https://github.com
##
## Public Health Agency, Sweden (2020), https://oppnadata.se
##
## Ministery of Health, Slovenia (2020), https://www.gov.si
##
## Open Government Data, Latvia (2020), https://data.gov.lv
##
## Open Government Data, Liechtenstein (2020), https://github.com
##
## Ministero della Salute, Italia (2020), https://github.com
##
## COVID19-India API (2020), https://www.covid19india.org
##
## OpenCOVID19 France (2020), https://github.com
##
## Wikipedia (2020), https://www.wikipedia.org
##
## Ministery of Health of Czech Republic (2020),
## https://onemocneni-aktualne.mzcr.cz
##
## Ministerio de Salud y Protección Social de Colombia (2020),
## https://www.datos.gov.co
##
## Swiss Federal Statistical Office (2018), https://www.bfs.admin.ch
##
## Public Health Infobase, Government of Canada (2020),
## https://health-infobase.canada.ca
##
## Epistat, Belgian Infectious Diseases (2020),
## https://epistat.sciensano.be
##
## Open Government Data, Austria (2020),
## https://info.gesundheitsministerium.at
##
## CIA - Central Intelligence Agency (2020), https://www.cia.gov
##
## Our World in Data (2020), https://github.com
##
## World Bank Open Data (2018), https://data.worldbank.org
##
## Hale Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz
## Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik
## School of Government.
##
## Johns Hopkins Center for Systems Science and Engineering (2020),
## https://github.com
##
## Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Working paper,
## doi: 10.13140/RG.2.2.11649.81763.
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.
##
## To hide the data sources use 'verbose = FALSE'.
# amr_x2という名前のオブジェクトに5/07現在のデータのみを取り出してしまう.
amr_x2 <- x %>%
filter(date=="2020-05-07")
datatable(amr_x2, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
wb <- c("gdp" = "NY.GDP.MKTP.CD", "hosp_beds" = "SH.MED.BEDS.ZS")
gmr <- "https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv"
wb_gmr_data <- covid19(gmr = gmr,
wb = wb)
##
## Data Science for Social Impact research group, University of Pretoria
## (2020), https://github.com
##
## Public Health Agency, Sweden (2020), https://oppnadata.se
##
## Ministery of Health, Slovenia (2020), https://www.gov.si
##
## Open Government Data, Latvia (2020), https://data.gov.lv
##
## Open Government Data, Liechtenstein (2020), https://github.com
##
## Ministero della Salute, Italia (2020), https://github.com
##
## COVID19-India API (2020), https://www.covid19india.org
##
## OpenCOVID19 France (2020), https://github.com
##
## Wikipedia (2020), https://www.wikipedia.org
##
## Ministery of Health of Czech Republic (2020),
## https://onemocneni-aktualne.mzcr.cz
##
## Ministerio de Salud y Protección Social de Colombia (2020),
## https://www.datos.gov.co
##
## Swiss Federal Statistical Office (2018), https://www.bfs.admin.ch
##
## Public Health Infobase, Government of Canada (2020),
## https://health-infobase.canada.ca
##
## Epistat, Belgian Infectious Diseases (2020),
## https://epistat.sciensano.be
##
## Open Government Data, Austria (2020),
## https://info.gesundheitsministerium.at
##
## CIA - Central Intelligence Agency (2020), https://www.cia.gov
##
## Our World in Data (2020), https://github.com
##
## World Bank Open Data (2018), https://data.worldbank.org
##
## Hale Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz
## Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik
## School of Government.
##
## Johns Hopkins Center for Systems Science and Engineering (2020),
## https://github.com
##
## Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Working paper,
## doi: 10.13140/RG.2.2.11649.81763.
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.
##
## To hide the data sources use 'verbose = FALSE'.
# wb_gmr_data2という名前のオブジェクトに5/7のデータのみを取り出してしまう.
wb_gmr_data2 <- wb_gmr_data %>%
filter(date=="2020-05-07")
datatable(wb_gmr_data2, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
# Data sources
s <- attr(wb_gmr_data2, "src")
datatable(s, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
* どこから持ってきたデータか. - いっぱいあって見にくいので,テーブル形式で見やすいようにした.
write.csv(wb_gmr_data2, "corona_data.csv")
# 必要に応じて
# install.packages("ggplotgui")
#library(ggplotgui)
#ggplot_shiny(wb_gmr_data2)
# install.packages("devtools"):初回だけ
# devtools::install_github("RamiKrispin/coronavirus"):初回だけ
# 毎回データのリフレッシュが必要
library(coronavirus)
update_dataset()
## Updates are available on the coronavirus Dev version, do you want to update? n/Y
datatable(coronavirus, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
library(dplyr)
summary_df <- coronavirus %>%
filter(type == "confirmed") %>%
group_by(country) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases)
datatable(summary_df, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
library(tidyr)
coronavirus %>%
filter(date == max(date)) %>%
select(country, type, cases) %>%
group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total_cases) %>%
arrange(-confirmed)
## # A tibble: 188 x 4
## # Groups: country [188]
## country confirmed death recovered
## <chr> <int> <int> <int>
## 1 US 25050 1632 4333
## 2 Brazil 17126 963 5491
## 3 Russia 10598 113 4696
## 4 Peru 3891 125 1996
## 5 India 3787 104 2289
## 6 United Kingdom 3564 385 4
## 7 Pakistan 3011 64 1185
## 8 Chile 2502 26 959
## 9 Mexico 2437 290 1976
## 10 Saudi Arabia 2307 9 2818
## # … with 178 more rows
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
coronavirus %>%
group_by(type, date) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type, values_from = total_cases) %>%
arrange(date) %>%
mutate(active = confirmed - death - recovered) %>%
mutate(active_total = cumsum(active),
recovered_total = cumsum(recovered),
death_total = cumsum(death)) %>%
plot_ly(x = ~ date,
y = ~ active_total,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace(y = ~ death_total,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(y = ~recovered_total,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Distribution of Covid19 Cases Worldwide",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Johns Hopkins University Center for Systems Science and Engineering"))
conf_df <- coronavirus %>%
filter(type == "confirmed") %>%
group_by(country) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases) %>%
mutate(parents = "Confirmed") %>%
ungroup()
plot_ly(data = conf_df,
type= "treemap",
values = ~total_cases,
labels= ~ country,
parents= ~parents,
domain = list(column=0),
name = "Confirmed",
textinfo="label+value+percent parent")
datatable(wb_gmr_data2, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))